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Given the success of the deep convolutional neural networks (DCNNs) in applications of visual recognition and classification, it would be tantalizing to test if DCNNs can also learn spatial concepts, such as straightness, convexity, left/right, front/back, relative size, aspect ratio, polygons, etc., from varied visual examples of these concepts that are simple and yet vital for spatial reasoning.
Understanding natural language
T. Winograd · 1962
Earlier work this paper cites.
The hippocampus as a cognitive map
J. O’Keefe and L. Nadel · 1978
Earlier work this paper cites.
Ordering of the numerosities 1 to 9 by monkeys
E. M. Brannon and H. S. Terrace · 1998
Earlier work this paper cites.
Place cells, navigational accuracy, and the human hippocampus
J. O’Keefe, N. Burgess, J. G. Donnett, K. J. Jeffery, and E. A. Maguire · 1998
Earlier work this paper cites.
Coding of cognitive magnitude: Compressed scaling of numerical information in the primate prefrontal cortex
A. Nieder and E. K. Miller · 2003
Earlier work this paper cites.
Numerosity discrimination in infants: Evidence for two systems of representations
F. Xu · 2003
Earlier work this paper cites.
A visual sense of number
D. Burr and J. Ross · 2008
Earlier work this paper cites.
The number sense: How the mind creates mathematics
S. Dehaene · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
The winograd schema challenge
H. J. Levesque, E. Davis, and L. Morgenstern · 2012
Cited alongside, same era.
Topographic representation of numerosity in the human parietal cortex
B. M. Harvey, B. P. Klein, N. Petridou, and S. O. Dumoulin · 2013
Cited alongside, same era.
Can winograd schemas replace turing test for defining human-level ai
E. Ackerman · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Our brains have a map for numbers
E. Reas · 2014
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Later among the works it cites.
Deepid3: Face recognition with very deep neural networks
Y. Sun, D. Liang, X. Wang, and X. Tang · 2015
Later among the works it cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, et al · 2015
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Later among the works it cites.
Cognitive psychology for deep neural networks: A shape bias case study
S. Ritter, D. G. Barrett, A. Santoro, and M. M. Botvinick · 2017
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K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Deep learning face representation by joint identification-verification
Y. Sun, Y. Chen, X. Wang, and X. Tang · 2014
Cited alongside, same era.
Later among the works it cites.
Cognitive deficit of deep learning in numerosity
X. Wu, X. Zhang, and X. Shu · 2018
Later among the works it cites.